rm(list=ls())
gc()

## 06 - ESTIMATE TSMART PID MODELS


## LOAD PACKAGES
library(data.table)
library(lfe)
library(stringr)

data = fread('panel-analysis-2016-2020.csv.gz',select = c('DemDiff','RepDiff','DemExpDiff','DemExp_year1','RepExpDiff','RepExp_year1','Age_year1','Race','Gender',
                                                                            'ZipCode','countyfips','Party_year1','hh.n.adj_year1','hh.n.adj_year2',
                                                                            'hh.d.adj_year1','hh.d.adj_year2','hh.r.adj_year1','hh.r.adj_year2',
                                                                            'Married_year1','State', 'WhiteBlockGroupDiff','MarriageDiff','AgeBlockGroupDiff','RegsBlockGroupDiff',
                                                                              'HHIncomeBlockGroupDiff' , 'CollegeBlockGroupDiff' , 'HomeownerBlockGroupDiff' , 'YearBuiltBlockGroupDiff' , 
                                                                              'DriveWorkBlockGroupDiff' , 'EmplBlockGroupDiff' , 'HouseValueBlockGroupDiff'))
  
  
  data[!Party_year1%in%c('Democrat','Republican'), Party_year1:='Other']
  data[countyfips=='',countyfips:=NA]
  data[ZipCode=='',ZipCode:=NA]
  data[Gender=='',Gender:=NA]
  
  
data[,hh.n.diff:=hh.n.adj_year2-hh.n.adj_year1]
data[,hh.d.diff:=hh.d.adj_year2-hh.d.adj_year1]
data[,hh.r.diff:=hh.r.adj_year2-hh.r.adj_year1]


  # split data
  dems = data[Party_year1=='Democrat']
  reps = data[Party_year1=='Republican']
  oths = data[Party_year1=='Other']
  
  rm(data)
  gc()
  
  # construct grouping variables 
  # we can drop rows where any of these are missing
  
  
  dems = dems[!is.na(Age_year1) & !is.na(Gender) & !is.na(Race) & !is.na(Married_year1) &!is.na(ZipCode)]
  reps = reps[!is.na(Age_year1) & !is.na(Gender) & !is.na(Race) & !is.na(Married_year1) & !is.na(ZipCode)]
  oths = oths[!is.na(Age_year1) & !is.na(Gender) & !is.na(Race) & !is.na(Married_year1) & !is.na(ZipCode)]
  
  dems[,AgeDecile:=cut(Age_year1,breaks=unique(as.numeric(quantile(Age_year1, probs=seq(0,1,by=.1),na.rm=T))),include.lowest=T)]
  reps[,AgeDecile:=cut(Age_year1,breaks=unique(as.numeric(quantile(Age_year1, probs=seq(0,1,by=.1),na.rm=T))),include.lowest=T)]
  oths[,AgeDecile:=cut(Age_year1,breaks=unique(as.numeric(quantile(Age_year1, probs=seq(0,1,by=.1),na.rm=T))),include.lowest=T)]
  

  # spatial seg treatment
  # democrat exposure
  dems[,DemSpDecile:=cut(DemExp_year1,breaks=unique(as.numeric(quantile(DemExp_year1, probs=seq(0,1,by=.1),na.rm=T))),include.lowest=T)]
  reps[,DemSpDecile:=cut(DemExp_year1,breaks=unique(as.numeric(quantile(DemExp_year1, probs=seq(0,1,by=.1),na.rm=T))),include.lowest=T)]
  oths[,DemSpDecile:=cut(DemExp_year1,breaks=unique(as.numeric(quantile(DemExp_year1, probs=seq(0,1,by=.1),na.rm=T))),include.lowest=T)]
  
# republican exposure
dems[,RepSpDecile:=cut(RepExp_year1,breaks=unique(as.numeric(quantile(RepExp_year1, probs=seq(0,1,by=.1),na.rm=T))),include.lowest=T)]
reps[,RepSpDecile:=cut(RepExp_year1,breaks=unique(as.numeric(quantile(RepExp_year1, probs=seq(0,1,by=.1),na.rm=T))),include.lowest=T)]
oths[,RepSpDecile:=cut(RepExp_year1,breaks=unique(as.numeric(quantile(RepExp_year1, probs=seq(0,1,by=.1),na.rm=T))),include.lowest=T)]


  
  #####
  # make groups
  # spatial seg treatment
  dems[,GroupSpDemExp:=paste(ZipCode,AgeDecile,Race, Gender, DemSpDecile,State,hh.n.adj_year1,hh.d.adj_year1,Married_year1, sep ='_')]
  reps[,GroupSpDemExp:=paste(ZipCode,AgeDecile,Race, Gender, DemSpDecile,State,hh.n.adj_year1,hh.d.adj_year1,Married_year1, sep ='_')]
  oths[,GroupSpDemExp:=paste(ZipCode,AgeDecile,Race, Gender, DemSpDecile,State,hh.n.adj_year1,hh.d.adj_year1,Married_year1, sep ='_')]
  
dems[,GroupSpRepExp:=paste(ZipCode,AgeDecile,Race, Gender, RepSpDecile,State,hh.n.adj_year1,hh.r.adj_year1,Married_year1, sep ='_')]
reps[,GroupSpRepExp:=paste(ZipCode,AgeDecile,Race, Gender, RepSpDecile,State,hh.n.adj_year1,hh.r.adj_year1,Married_year1, sep ='_')]
oths[,GroupSpRepExp:=paste(ZipCode,AgeDecile,Race, Gender, RepSpDecile,State,hh.n.adj_year1,hh.r.adj_year1,Married_year1, sep ='_')]

  # estimate models
  
  # spatial seg treatment
  ModelDemExpDems = summary(felm(DemDiff ~ DemExpDiff + hh.d.diff+hh.n.diff+WhiteBlockGroupDiff+MarriageDiff+AgeBlockGroupDiff+RegsBlockGroupDiff+
                                     HHIncomeBlockGroupDiff + CollegeBlockGroupDiff + HomeownerBlockGroupDiff + YearBuiltBlockGroupDiff + 
                                     DriveWorkBlockGroupDiff + EmplBlockGroupDiff + HouseValueBlockGroupDiff|GroupSpDemExp|0|countyfips, data = dems[!grepl('_NA_',GroupSpDemExp)&!is.na(countyfips)]), robust =T)
  ModelDemExpReps = summary(felm(DemDiff ~ DemExpDiff+ hh.d.diff+hh.n.diff+WhiteBlockGroupDiff+MarriageDiff+AgeBlockGroupDiff+RegsBlockGroupDiff+
                                     HHIncomeBlockGroupDiff + CollegeBlockGroupDiff + HomeownerBlockGroupDiff + YearBuiltBlockGroupDiff + 
                                     DriveWorkBlockGroupDiff + EmplBlockGroupDiff + HouseValueBlockGroupDiff|GroupSpDemExp|0|countyfips, data = reps[!grepl('_NA_',GroupSpDemExp)&!is.na(countyfips)]), robust =T)
  ModelDemExpOths = summary(felm(DemDiff ~ DemExpDiff+ hh.d.diff+hh.n.diff+WhiteBlockGroupDiff+MarriageDiff+AgeBlockGroupDiff+RegsBlockGroupDiff+
                                     HHIncomeBlockGroupDiff + CollegeBlockGroupDiff + HomeownerBlockGroupDiff + YearBuiltBlockGroupDiff + 
                                     DriveWorkBlockGroupDiff + EmplBlockGroupDiff + HouseValueBlockGroupDiff|GroupSpDemExp|0|countyfips, data = oths[!grepl('_NA_',GroupSpDemExp)&!is.na(countyfips)]), robust =T)
  
  ModelRepExpDems = summary(felm(RepDiff ~ RepExpDiff+ hh.r.diff+hh.n.diff+WhiteBlockGroupDiff+MarriageDiff+AgeBlockGroupDiff+RegsBlockGroupDiff+
                                     HHIncomeBlockGroupDiff + CollegeBlockGroupDiff + HomeownerBlockGroupDiff + YearBuiltBlockGroupDiff + 
                                     DriveWorkBlockGroupDiff + EmplBlockGroupDiff + HouseValueBlockGroupDiff|GroupSpRepExp|0|countyfips, data = dems[!grepl('_NA_',GroupSpRepExp)&!is.na(countyfips)]), robust =T)
  ModelRepExpReps = summary(felm(RepDiff ~ RepExpDiff+ hh.r.diff+hh.n.diff+WhiteBlockGroupDiff+MarriageDiff+AgeBlockGroupDiff+RegsBlockGroupDiff+
                                     HHIncomeBlockGroupDiff + CollegeBlockGroupDiff + HomeownerBlockGroupDiff + YearBuiltBlockGroupDiff + 
                                     DriveWorkBlockGroupDiff + EmplBlockGroupDiff + HouseValueBlockGroupDiff|GroupSpRepExp|0|countyfips, data = reps[!grepl('_NA_',GroupSpRepExp)&!is.na(countyfips)]), robust =T)
  ModelRepExpOths = summary(felm(RepDiff ~ RepExpDiff+ hh.r.diff+hh.n.diff+WhiteBlockGroupDiff+MarriageDiff+AgeBlockGroupDiff+RegsBlockGroupDiff+
                                     HHIncomeBlockGroupDiff + CollegeBlockGroupDiff + HomeownerBlockGroupDiff + YearBuiltBlockGroupDiff + 
                                     DriveWorkBlockGroupDiff + EmplBlockGroupDiff + HouseValueBlockGroupDiff|GroupSpRepExp|0|countyfips, data = oths[!grepl('_NA_',GroupSpRepExp)&!is.na(countyfips)]), robust =T)
  
  
  
  save(ModelDemExpDems, ModelDemExpReps, ModelDemExpOths, 
       ModelRepExpDems, ModelRepExpReps, ModelRepExpOths,
       
       
       file = paste0('results/current-results-2016-2020-aspatial.Rdata'))
  
